A dependency parser can take a tokenized input sentence (for example, part-of-speech tagged) and produce a connected tree (a parse tree) where directed arcs represent a syntactic head-modifier relationship. An example of such a tree is shown in FIG. 1. Dependency tree arcs are often labeled with the role of the syntactic relationship, e.g., is to hearing might be labeled as SUBJECT.
A parser can be trained by generating a predicted parse for a given input sentences in a given target language and comparing the predicted parse with an annotated, gold standard output that corresponds to the input. For example, a dependency parse that is generated by the system for a given sentence can be compared to a parse that was hand-annotated by a human expert for the same sentence. The parameter vector used by the parser to generate the parse can then be tuned based upon the differences between the generated parse and the gold standard parse. Such supervised training improves the likelihood that the parser will generate of more accurate parses for subsequent input sentences. Some languages lack labeled data (e.g., gold standard data) for training a parser.